Biology subjects

Jones, M. T.

Publications and source records attributed to Jones, M. T..

3 recordsLinked to original sources

bioRxiv2026

Local translation couples synaptic activity to mitochondrial adaptation in dendrites

Neurons rely on localized protein synthesis to rapidly adapt synaptic function to activity, yet how dendritic translation regulates mitochondrial remodeling during synaptic plasticity remains poorly understood. Here, we show that neuronal activity engages a spatially restricted translational program that couples local protein synthesis to mitochondrial function through the non-canonical translation initiation factor eIF4G2. Using proximity labeling to profile the dendritic RNA interactome, translatome, and proteome, we identify a cohort of nuclear-encoded mitochondrial mRNAs that are selectively recruited for translation following depolarization and mGluR activation. This program drives activity-dependent increases in mitochondrial membrane potential, mitochondrial abundance, and oxygen consumption. Loss of eIF4G2 abolishes these responses, whereas dendrite-specific, but not soma-restricted, rescue restores mitochondrial remodeling, demonstrating that eIF4G2 functions locally at postsynaptic sites. Mechanistically, eIF4G2 binds the 5 prime or minute untranslated regions of activity-responsive mitochondrial transcripts and promotes translation of both upstream open reading frames (uORFs) and downstream coding sequences. Using a dendritically targeted split-GFP reporter, we further show that neuronal activity induces local uORF translation to generate previously unannotated micropeptides. Together, our findings identify eIF4G2-dependent local translation as a mechanism that establishes mitochondrial competence during synaptic activity by coordinating the production of mitochondrial proteins and uORF-encoded micropeptides.

neuroscience↗

bioRxiv2025

Improved Estimation of Correlation Accuracy for Machine Learning Brain-Phenotype Associations

Machine learning is used in neuroscience to examine brain-phenotype associations and facilitate individual prediction from high-dimensional brain imaging. For continuous phenotypes, Pearsons correlation between the observed and predicted phenotype is used to quantify model accuracy in testing data. However, recent research suggests millions of samples may be needed to reliably estimate the maximum achievable predictive accuracy (MAPA). We formally define the MAPA and show that Pearsons estimator is biased for this quantity and its confidence intervals fail to capture the target. We develop a semiparametric (double machine learning) one-step estimator that more accurately estimates the MAPA and yields valid confidence intervals across flexible machine learning settings. Analyzing data from the Reproducible Brain Charts dataset, we show that this estimator has smaller bias when estimating brain-phenotype associations of neuroimaging data with age and psychopathology phenotypes. We show that MAPA for psychopathology factor scores using machine learning models built on structural and functional imaging measures is not better than using demographic and nuisance covariates alone.

neuroscience↗

bioRxiv2023

Study design features that improve effect sizes in cross-sectional and longitudinal brain-wide association studies

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required for good replicability of BWAS because the standardized effect sizes (ESs) are much smaller than the reported standardized ESs in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized ESs and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability in covariate have larger reported standardized ES. In addition, the longitudinal studies we examined reported systematically larger standardized ES than cross-sectional studies. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimers Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes improves the standardized ESs and replicability. Sampling schemes that improve standardized ESs and replicability include increasing between-subject age variability in the sample and adding a single additional longitudinal measurement per subject. To ensure that our results are generalizable, we further evaluate these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrate that commonly used longitudinal models can, counterintuitively, reduce standardized ESs and replicability. The benefit of conducting longitudinal studies depends on the strengths of the between-versus within-subject associations of the brain and non-brain measures. Explicitly modeling between-versus within-subject effects avoids averaging the effects and allows optimizing the standardized ESs for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.

neuroscience↗